Rosternomics
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May 9, 2018

CLETOR

CLE won this trade +$1.6M surplus CLE won this trade +0.1 WAR
CLECLE Chris Antonetti net +$1.6M net +0.1
received +$0.0M+$0.0M ± $0M expected surplus · +$0.0M realized received 0.0 ± 0 expected · 0.0 realized WAR
Playoff odds: this deal moved CLE's 2018 odds 81% → 81% (+0.4 pts) — how trade timing is graded ↗
receives — most valuable first
cash / PTBNL
+$0.0M+$0.0M± $0M exp surplusrealized +$0.0M 0.0± 0 exp WARrealized 0.0
Cash or player to be named — no projection
TORTOR Ross Atkins net −$1.6M net -0.1
received −$2.4M−$2.4M ± $29M expected surplus · −$1.6M realized received 0.0 ± 4 expected · -0.1 realized WAR
Playoff odds: this deal moved TOR's 2018 odds 2% → 2% (-0 pts) — how trade timing is graded ↗
receives — most valuable first
Gio Urshela3B·27y·R/R
−$2.4M−$2.4M± $29M exp surplusrealized −$1.6M 0.0± 4 exp WARrealized -0.1
Prior
no pedigree — league baseline → 0.21/yr
Evidence
recent form -1.3/yr over 0.7 season
Talent
-0.33/yr blended
Horizon
3.0 control yrs

Each player is valued on what he was expected to produce at the time of the trade, versus what he actually produced for his new team.

Expected WAR blends a player's pedigree (Baseball America rank / draft slot, or a baseline) with his recent track record, projected over the years of team control acquired. The ± band is the uncertainty — wide for unproven prospects, tight for established veterans. Surplus values that production at the FA market price of a win (~$8M/WAR) minus salary — so cost-controlled players carry large surplus and expensive ones little, even at the same WAR. Who won is descriptive, not a skill claim: ~99% of a trade's outcome is unforeseeable at the time.

Historically these expected values are unbiased and land within ±2 WAR of reality 75% of the time — yet the side the model favors actually comes out ahead only 53% of the time. The grade is a calibrated bet, not a prediction. Why trades are an efficient market →